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针对图像滤波时损失图像细节这一问题,提出了一种自适应多尺度形态滤波方法,在普通多尺度形态开、闭滤波基础上增加了多尺度top hat变换和bottom hat变换,用于提取并平滑小尺度的图像信息。top hat变换和bottom hat变换的系数对整个滤波器性能起着重要的作用,采用遗传优化的方法对其进行优化。实验结果表明,该方法噪声去除效果好,图像细节保持完整,提高了输出图像的信噪比,增强了滤波器的自适应性和智能性,处理效果明显优于传统滤波方法。
Aiming at the loss of image details in image filtering, an adaptive multi-scale morphological filtering method is proposed, which adds multi-scale top hat transform and bottom hat transform based on common multi-scale morphological opening and closing filters for extracting Smooth small-scale image information. The coefficients of top hat transform and bottom hat transform play an important role in the performance of the whole filter, and are optimized by genetic optimization. The experimental results show that this method has a good noise removal effect and a complete image detail, improves the signal-to-noise ratio of the output image, and enhances the adaptability and intelligence of the filter. The processing effect is obviously better than the traditional filtering method.